import math

import torch.nn as nn
import torch.utils.model_zoo as model_zoo


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class SEBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(SEBasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

        self.se = nn.Sequential(
            nn.Linear(planes, planes // 16, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(planes // 16, planes, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        out_pooled = nn.functional.adaptive_avg_pool2d(out, output_size=1)
        out_pooled = out_pooled.view(out_pooled.size(0), -1)
        weight = self.se(out_pooled)
        weight = weight.view(weight.size(0), weight.size(1), 1, 1)
        out = weight * out

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class SEBottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(SEBottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

        self.se = nn.Sequential(
            nn.Linear(planes * 4, planes // 4, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(planes // 4, planes * 4, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        out_pooled = nn.functional.adaptive_avg_pool2d(out, output_size=1)
        out_pooled = out_pooled.view(out_pooled.size(0), -1)
        weight = self.se(out_pooled)
        weight = weight.view(weight.size(0), weight.size(1), 1, 1)
        out = weight * out

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}

cfg = {
    18: [SEBasicBlock, [2, 2, 2, 2]],
    34: [SEBasicBlock, [3, 4, 6, 3]],
    50: [SEBottleneck, [3, 4, 6, 3]],
    101: [SEBottleneck, [3, 4, 23, 3]],
    152: [SEBottleneck, [3, 8, 36, 3]]
}


class SEResNet(nn.Module):

    def __init__(self, num_layers, num_classes=1000, pretrained=False, freeze_stages=-1, strides=[1, 2, 2, 2],
                 init_weights=True):
        self.inplanes = 64
        super(SEResNet, self).__init__()
        assert freeze_stages >= -1 and freeze_stages <= 3, "Invalid argument freeze_stages {}".format(freeze_stages)
        self.num_classes = num_classes
        self.num_layers = num_layers

        feat_name = "resnet%d" % (num_layers)
        assert feat_name in model_urls, "{} is not supported!".format(feat_name)
        block, layers = cfg[num_layers]

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=strides[3])

        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        if pretrained:
            self._load_weights(feat_name)
        else:
            if init_weights:
                self._init_weights()

        self._freeze_stages(freeze_stages)

    def _freeze_stages(self, freeze_stages):
        if freeze_stages != -1:
            for layer in [self.conv1, self.bn1]:
                for param in layer.parameters():
                    param.require_grad = False

            for i in range(1, freeze_stages + 2):
                layer = getattr(self, "layer%d" % (i))
                for param in layer.parameters():
                    param.require_grad = False

    def _init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _load_weights(self, feat_name):
        state_dict = model_zoo.load_url(model_urls[feat_name])

        if self.num_classes == 1000:
            self.load_state_dict(state_dict)
        else:
            state_dict.pop('fc.weight')
            state_dict.pop('fc.bias')
            self.load_state_dict(state_dict, strict=False)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x
